The invention relates generally to methods and an apparatus for identifying regions in images, and more particularly to methods and apparatus for labeling anatomical structures in medical images.
In many imaging applications, specifically medical imaging applications, the images are often analyzed to identify various anatomical structures such as organs, lesions, etc. For example, a chest X-ray radiograph or a computed tomography (CT) image can be employed to facilitate the detection of lung cancer. Specifically, CT images advantageously provide a description of anatomy in great detail and consequently is being increasingly are used for detecting and following the evolution of lesions that may lead to potential cancers.
For example, for the detection of lung and colon cancer, radiologists search for the presence of nodules and polyps in the lung and colon using advanced lung analysis (ALA) and computed tomographic colonography (CTC) techniques. Radiologists detect nodules in the lung by viewing axial slices of the chest. However, CT systems generally provide several images for a single CT scan. Consequently, a considerable amount of information is presented to the radiologist for use in interpreting the images and detecting suspect regions that may indicate disease. The considerable amount of data associated with a single CT scan presents a time-consuming process to the radiologist. Furthermore, this substantial amount of data may disadvantageously lead to missed cancer detection, as it is difficult to identify a suspicious area in an extensive amount of data. In addition, the sheer size of the CT volumes results in significant variability in radiological readings and clinically important nodules are missed.
Techniques variously described as computer aided detection, or computer assisted detection or computer assisted diagnosis, and often referred to by the acronym “CAD” have emerged as a viable approach for aiding the radiologists in the detection of lung nodules in chest radiographs and thoracic CT scans, as well as for detecting and diagnosing other anatomies and disease states.
Several CAD techniques have been developed to highlight the anatomical structures present in various regions in the image. In one specific technique, the regions are identified and labeled according to the local shape of their surrounding structures. In one more specific technique used to identify lung cancer, an Eigen analysis of a Hessian matrix is used as a tool to classify voxels as belonging to a vessel or a nodule. However, such techniques consider a very small neighborhood of each voxel needed to compute image derivatives and thus are not very robust and/or accurate in identifying the structures.
It may therefore be desirable to develop a robust technique and system for processing image data that advantageously facilitates substantially superior initial shape-based identification of regions in an image that can be consequently used for the analysis of the object being examined.